Description
<p><p>Mistplay est lapplication de fidélité n°1 pour les joueurs mobiles. Notre communauté de millions de joueurs mobiles engagés utilise Mistplay pour découvrir de nouveaux jeux et gagner des récompenses. Les joueurs sont récompensés pour le temps et largent quils consacrent aux jeux et peuvent échanger ces récompenses contre des cartes cadeaux. Mistplay a pour mission dêtre le meilleur moyen de jouer à des jeux mobiles pour tous, partout dans le monde ! Téléchargez Mistplay sur le Google Play Store. </p> <p>Veuillez noter : Au Canada, Mistplay suit un modèle hybride de 2 jours/semaine en bureau à Toronto (400 University Ave) et Montréal (1001 Blvd. Robert-Bourassa). </p> <p>Reporting to the VP of Data and Machine Learning Platform, the Staff ML Platform Engineer within Mistplay’s Data Team will play a key role in researching and developing machine learning solutions to solve complex business problems. The Staff ML Platform Engineer will work closely with a cross‑functional team to identify areas for improvement and design and implement scalable solutions. </p> <h3>What you’ll do: </h3> <ul> <li>Be the main driver and expert for designing, building, and operating machine and data infrastructure solutions for training models </li> <li>Real‑time inference systems to operate and serve models in a real‑time production environment. </li> <li>High usability and accuracy feature platform capabilities for generating, backfilling and storing user‑level features. </li> <li>High accuracy low latency feature serving layer and preprocessing solutions to support online serving of the models. </li> <li>Build platform abstractions and golden paths: Airflow DAG templates, CLI/SDKs, cookie‑cutter repos, and CI/CD pipelines that take models from notebooks to production predictably. </li> <li>Implement end‑to‑end observability: data/feature freshness checks, drift/quality gates, model performance/latency SLOs, infra health dashboards, tracing, and alerting—plus incident response and postmortems. </li> <li>Partner with Security, SRE, and Data Engineering on private networking, policy‑as‑code, PII handling, least‑privilege IAM, and cost‑efficient architectures across environments. </li> <li>Evaluate, integrate, and rationalize platform tooling (e.g., MLflow registry, feature stores, serving gateways); lead migrations with clear change management and minimal downtime. </li> </ul> <h3>What you’ll bring: </h3> <ul> <li>10+ years building and operating production‑grade ML/Data platforms with a focus on serving, reliability, and developer experience. </li> <li>Strong software engineering skills in Python, Go or Java; experience building resilient services, APIs, and automation tooling with high test coverage. </li> <li>Deep experience with inference solutions: endpoint configuration, containerization, model packaging, autoscaling, serverless vs. real‑time trade‑offs, MME, A/B and canary releases. </li> <li>Expertise with online feature store paradigms and the underlying storage solutions in ML serving contexts. </li> <li>Proven Terraform experience managing ML and data infra end‑to‑end: modules, workspaces, drift detection, change reviews, and safe rollbacks; familiarity with GitOps patterns. </li> <li>Airflow orchestration at scale: dependency modeling, sensors, retries, SLAs, backfills, DAG factories, and integrations with registries, artifact stores, and Terraform pipelines. </li> <li>Familiarity with ML frameworks (scikit‑learn, XGBoost, PyTorch, TensorFlow) from a platform‑integration perspective to support diverse runtimes and containers. </li> <li>Observability for ML workflows: metrics/logs/traces, performance profiling, capacity planning, cost monitoring, and runbooks. </li> <li>Excellent communication and cross‑functional collaboration with Data Science, Data Engineering, DevOps and Backend. </li> </ul> <p>*Nous remercions tous(tes) les candidat(e)s. Le genre masculin a été utilisé dans le but dalléger le texte. Nous souscrivons au principe de l’équité en matière d’emploi. </p> </p> #J-18808-Ljbffr
